Missing Data Recovery by Exploiting Low-Dimensionality in Power System Synchrophasor Measurements

This paper presents a new framework of recovering missing synchrophasor measurements (erasures). Leveraging the approximate low-rank property of phasor measurement unit (PMU) data, we connect the problem of recovering PMU data erasures with recent advances in low-rank matrix completion methods. Since the existing analysis for matrix completion methods assumes an independent-erasure model that does not capture the correlations in PMU erasures, we propose two models to characterize the temporal and the channel correlations in PMU erasures and provide theoretical guarantees of a matrix completion method in recovering correlated erasures in both models. We also propose an online algorithm that can fill in the missing PMU measurements for real-time applications. Numerical experiments on actual PMU data are conducted to verify the effectiveness of the proposed methods.